Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification

Meng, Yanmei; Yu, Shuangshuang; Wang, Hui; Qin, Johnny; Xie, Yanpeng

doi:10.1002/fsn3.985

Cited by 6 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To verify the effectiveness and superiority of the proposed model, it is analyzed and compared with widely used data‐driven modeling methods such as RBF (Ghritlahre & Prasad, 2018), SVM (Han et al, 2005), ELM (He et al, 2015), KELM (Meng, Lan, et al, 2019; Meng, Yu, et al, 2019), and ML‐ELM (Kasun et al, 2013). And all data‐driven model is trained using the common hybrid chicken swarm optimization approach.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…de Souza Sartori et al (2017) introduced the artificial neural network (ANN) model into the modeling of the cane juice clarification process to predict the influence of different variables on the color value and sucrose content of cane juice. Meng, Lan, et al (2019) and Meng, Yu, et al (2019) proposed a twin support vector machine to predict mother liquor supersaturation and mother liquor purity using seven easily measurable variables as the input. The milling system is a complex production process with strong coupling and nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Soft sensor with deep feature extraction for a sugarcane milling system

Meng

Chen

et al. 2022

J Food Process Engineering

Self Cite

View full text Add to dashboard Cite

Extraction rate and energy consumption are two important indicators of the sugarcane milling system, which are calculated by offline experiments and inspection methods. Soft sensors are inferential models, which are enabling online prediction of these indicators. Selection of input variables is one of the most critical issues in soft sensors design. So a deep feature extraction method is proposed by combining mutual information theory and hybrid chicken swamp algorithm to determine the input and parameters of model. Then, input the obtained variables into the data‐driven model of deep kernal extreme learning machine (DK‐ELM) with optimized parameters to realize the prediction of extraction and hourly energy consumption. It combines the kernel method with the multi‐layer ELM. Experiments are conducted to verify the effectiveness and superiority of the modeling method. Comparing the proposed framework with the four experiments designed on a sugarcane milling system, the proposed framework for predicting extraction and energy consumption with better feasibility and effectiveness, and provided a good reference for end‐point control and judgment of quick direct tapping. Practical Applications Extraction and energy consumption are two important indicators of the milling process and whether they meet standards will affect the smooth operation and economic benefits of sugar production. This study presents a novel modeling method to predict these indicators based on the deep feature extraction and deep kernal extreme learning machine. These methods provided a good reference for end‐point control and judgment of quick direct tapping. In addition, it a new concept for the design of soft sensors for such complex industrial processes.

show abstract

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Soft sensor with deep feature extraction for a sugarcane milling system

Meng

Chen

et al. 2022

J Food Process Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sucipto et al (2018) tested the bioelectrical characteristics of sugarcane and adopted artificial neural network modeling to predict the moisture content, sucrose, and inverted sugar. Meng et al (2021, 2019) exploited Kernel Extreme Learning Machine (KELM) to predict the specific purity and color value of sugarcane juice in the clarification section and optimized the model parameters using a particle swarm optimization algorithm. Cardoso et al (2022) studied dehydration technology to improve the prediction ability of near‐infrared spectroscopy and partial least squares regression model for sucrose, glucose, and fructose in sugarcane juice.…”

Section: Introductionmentioning

confidence: 99%

Hybrid data‐driven modeling for accurate prediction of quality parameters in sugarcane milling with fine‐tuning

Yang,

Mao,

Yin

et al. 2023

J Food Process Engineering

View full text Add to dashboard Cite

The data‐driven model for predicting sugarcane milling quality parameters is crucial for optimization. Current challenges include real‐time data acquisition and limited labeled data, reducing model accuracy and raising data collection costs. There is an urgent need to utilize unlabeled data to establish data‐driven models for sugarcane milling. This study introduces novel hybrid strategies, namely combining Convolutional Neural Network (CNN) with Kernel Extreme Learning Machine (KELM), utilizing pre‐training and fine‐tuning techniques. These strategies include CNN‐KELM and Transfer Learning CNN‐KELM (TL‐CNN‐KELM). Results demonstrate superior accuracy compared to single algorithms. TL‐CNN‐KELM, leveraging unlabeled data through transfer learning, achieves reductions of 0.007 in mean squared error and 0.005 in mean absolute error for extraction rate predictions. For initial juice sugar content predictions, reductions of 0.027 in mean squared error and 0.012 in mean absolute error are attained. This approach demonstrates enhanced accuracy in predicting quality parameters while maintaining robustness to data size.Practical ApplicationsThis work can reduce the data acquisition cost for quality parameter prediction and accelerate the intellectualization of sugarcane milling. In production practice, there are many process industries similar to sugarcane milling, so the novel algorithms also provide an idea for the quality parameter optimization and prediction of other process industries.

show abstract

“…Song et al used the principal component analysis (PCA) method to process the production data and developed a generalized dynamic fuzzy neural network to predict the color value and acidity of the sucrose carbonation clarification process [ 12 ]. Meng et al proposed a data-driven model based on a kernel extreme learning machine (KELM) to predict the juice’s gravity purity and the clear juice’s color value [ 13 ]. Georgieva et al took mother-liquid oversaturation and other independent parameters as inputs, and chose crystal nucleation, growth, and aggregation as outputs, in order to establish an offline prediction model [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Sugarcane Milling System Operations Based on a Deep Data-Driven Model

Chen

Meng

et al. 2022

Foods

Self Cite

View full text Add to dashboard Cite

The extraction of sugarcane juice is the first step of sugar production. The optimal values of process indicators and the set values of operating parameters in this process are still determined by workers’ experience, preventing adaptive adjustment of the production process. To address this issue, a multi-objective optimization framework based on a deep data-driven model is proposed to optimize the operation of sugarcane milling systems. First, the sugarcane milling process is abstracted as the interaction of material flow, energy flow, and information flow (MF–EF–IF) by introducing synergetic theory, and each flow’s order parameters and state parameters are obtained. Subsequently, the state parameters of the subsystems are taken as inputs, and the order parameters—including the grinding capacity, electric consumption per ton of sugarcane, and sucrose extraction—are produced as outputs. A collaborative optimization model of the MF–EF–IF of the milling system is established by using a deep kernel extreme learning machine (DK-ELM). The established milling system model is applied for an improved multi-objective chicken swarm optimization (IMOCSO) algorithm to obtain the optimal values of the order parameters. Finally, the milling process is described as a Markov decision process (MDP) with the optimal values of the order parameters as the control objectives, and an improved deep deterministic policy gradient (DDPG) algorithm is employed to achieve the adaptive optimization of the operating parameters under different working conditions of the milling system. Computational experiments indicate that enhanced performance is achieved, with an increase of 3.2 t per hour in grinding capacity, a reduction of 660 W per ton in sugarcane electric consumption, and an increase of 0.03% in the sucrose extraction.

show abstract

Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification

Cited by 6 publications

References 28 publications

Soft sensor with deep feature extraction for a sugarcane milling system

Soft sensor with deep feature extraction for a sugarcane milling system

Hybrid data‐driven modeling for accurate prediction of quality parameters in sugarcane milling with fine‐tuning

Multi-Objective Optimization of Sugarcane Milling System Operations Based on a Deep Data-Driven Model

Contact Info

Product

Resources

About